Advertisement

Semantics of User Interaction in Social Media

  • Folke Mitzlaff
  • Martin Atzmueller
  • Gerd Stumme
  • Andreas Hotho
Part of the Studies in Computational Intelligence book series (SCI, volume 476)

Abstract

In ubiquitous and social web applications, there are different user traces, for example, produced explicitly by ”tweeting” via twitter or implicitly, when the corresponding activities are logged within the application’s internal databases and log files.

For each of these systems, the sets of user interactions can be mapped to a network, with links between users according to their observed interactions. This gives rise to a number of questions: Are these networks independent, do they give rise to a notion of user relatedness, is there an intuitively defined relation among users?

In this paper, we analyze correlations among different interaction networks among users within different systems. To address the questions of interrelationship between different networks, we collect for every user certain external properties which are independent of the given network structure. Based on these properties, we then calculate semantically grounded reference relations among users and present a framework for capturing semantics of user relations. The experiments are performed using different interaction networks from the twitter, flickr and BibSonomy systems.

Keywords

Similarity Score Semantic Similarity Online Social Network Cosine Similarity Link Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Brown, C., Nicosia, V., Scellato, S., Noulas, A., Mascolo, C.: Where Online Friends Meet: Social Communities in Location-based Networks. In: Proc. Sixth International AAAI Conference on Weblogs and Social Media (ICWSM 2012), Dublin, Ireland (2012)Google Scholar
  2. 2.
    Cattuto, C., Benz, D., Hotho, A., Stumme, G.: Semantic Grounding of Tag Relatedness in Social Bookmarking Systems. In: Sheth, A.P., Staab, S., Dean, M., Paolucci, M., Maynard, D., Finin, T., Thirunarayan, K. (eds.) ISWC 2008. LNCS, vol. 5318, pp. 615–631. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Cohen, T., Widdows, D.: Empirical distributional semantics: methods and biomedical applications. J. Biomed. Inform 42(2), 390–405 (2009)CrossRefGoogle Scholar
  4. 4.
    Grefenstette, G.: Finding semantic similarity in raw text: The deese antonyms. In: Fall Symposium Series, Working Notes, Probabilistic Approaches to Natural Language, pp. 61–65 (1992)Google Scholar
  5. 5.
    Hotho, A., Jäschke, R., Schmitz, C., Stumme, G.: Information Retrieval in Folksonomies: Search and Ranking. In: Sure, Y., Domingue, J. (eds.) ESWC 2006. LNCS, vol. 4011, pp. 411–426. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Islam, A., Inkpen, D.: Second order co-occurrence pmi for determining the semantic similarity of words. In: Proc. of the Int. Conference on Language Resources and Evaluation (LREC 2006), pp. 1033–1038 (2006)Google Scholar
  7. 7.
    Jeh, G., Widom, J.: Simrank: a measure of structural-context similarity. In: Proc. of the Eighth ACM SIGKDD Int. Conference on Knowledge Discovery and Data Mining, KDD 2002, pp. 538–543. ACM, New York (2002)CrossRefGoogle Scholar
  8. 8.
    Kaltenbrunner, A., Scellato, S., Volkovich, Y., Laniado, D., Currie, D., Jutemar, E.J., Mascolo, C.: Far From the Eyes, Close on the Web: Impact of Geographic Distance on Online Social Interactions. In: Proc. ACM SIGCOMM Workshop on Online Social Networks (WOSN 2012), Helsinki, Finland (2012)Google Scholar
  9. 9.
    Kulshrestha, J., Kooti, F., Nikravesh, A., Gummadi, K.: Geographic dissection of the twitter network. In: Proc. International AAAI Conference on Weblogs and Social Media (2012)Google Scholar
  10. 10.
    Kwak, H., Lee, C., Park, H., Moon, S.: What is twitter, a social network or a news media? In: Proceedings of the 19th International Conference on World Wide Web, pp. 591–600. ACM (2010)Google Scholar
  11. 11.
    Landauer, T., Dumais, S.: A solution to plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychological Review 104(2), 211 (1997)CrossRefGoogle Scholar
  12. 12.
    Leicht, E.A., Holme, P., Newman, M.E.J.: Vertex similarity in networks (2005), cite arxiv:physics/0510143Google Scholar
  13. 13.
    Lesk, M.: Word-word associations in document retrieval systems. American Documentation 20(1), 27–38 (1969)CrossRefGoogle Scholar
  14. 14.
    Li, N., Chen, G.: Analysis of a Location-Based Social Network. In: Proc. International Conference on Computational Science and Engineering, CSE 2009, pp. 263–270. IEEE Computer Society, Washington, DC (2009)CrossRefGoogle Scholar
  15. 15.
    Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. of the American Society for Inf. Science and Technology 58(7), 1019–1031 (2007)CrossRefGoogle Scholar
  16. 16.
    Lü, L., Jin, C., Zhou, T.: Similarity index based on local paths for link prediction of complex networks. Physical Review E 80(4), 046122 (2009)Google Scholar
  17. 17.
    Lü, L., Zhou, T.: Link prediction in weighted networks: The role of weak ties. EPL (Europhysics Letters) 89, 18001 (2010)CrossRefGoogle Scholar
  18. 18.
    McGee, J., Caverlee, J.A., Cheng, Z.: A Geographic Study of Tie Strength in Social Media. In: Proc. 20th ACM International Conference on Information and Knowledge Management, CIKM 2011, pp. 2333–2336. ACM, New York (2011)Google Scholar
  19. 19.
    Mitzlaff, F., Atzmueller, M., Benz, D., Hotho, A., Stumme, G.: Community Assessment Using Evidence Networks. In: Atzmueller, M., Hotho, A., Strohmaier, M., Chin, A. (eds.) MUSE/MSM 2010. LNCS, vol. 6904, pp. 79–98. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  20. 20.
    Mitzlaff, F., Benz, D., Stumme, G., Hotho, A.: Visit Me, Click Me, Be My Friend: An Analysis of Evidence Networks of User Relationships in Bibsonomy. In: Proceedings of the 21st ACM Conference on Hypertext and Hypermedia, Toronto, Canada (2010)Google Scholar
  21. 21.
    Mitzlaff, F., Stumme, G.: Relatedness of given names. Human Journal 1(4), 205–217 (2012)Google Scholar
  22. 22.
    de Sá, H., Prudencio, R.: Supervised link prediction in weighted networks. In: The 2011 Int. Joint Conference on Neural Networks (IJCNN), pp. 2281–2288. IEEE (2011)Google Scholar
  23. 23.
    Sadilek, A., Kautz, H., Bigham, J.P.: Finding Your Friends and Following Them to Where You Are. In: Proc. Fifth ACM International Conference on Web Search and Data Mining, WSDM 2012, pp. 723–732. ACM, New York (2012)CrossRefGoogle Scholar
  24. 24.
    Scellato, S., Noulas, A., Lambiotte, R., Mascolo, C.: Socio-spatial properties of online location-based social networks. In: Proceedings of ICWSM 2011, pp. 329–336 (2011)Google Scholar
  25. 25.
    Turney, P.D.: Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL. In: Flach, P.A., De Raedt, L. (eds.) ECML 2001. LNCS (LNAI), vol. 2167, pp. 491–502. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  26. 26.
    Weng, J., Lim, E., Jiang, J., He, Q.: Twitterrank: Finding Topic-Sensitive Influential Twitterers. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 261–270. ACM (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Folke Mitzlaff
    • 1
  • Martin Atzmueller
    • 1
  • Gerd Stumme
    • 1
  • Andreas Hotho
    • 2
  1. 1.Knowledge and Data Engineering GroupUniversity of KasselKasselGermany
  2. 2.Data Mining and Information Retrieval GroupUniversity of WürzburgWürzburgGermany

Personalised recommendations